System and method for determining car to lane distance

ABSTRACT

A system and method for determining car to lane distance is provided. In one aspect, the system includes a camera configured to generate an image, a processor, and a computer-readable memory. The processor is configured to receive the image from the camera, generate a wheel segmentation map representative of one or more wheels detected in the image, and generate a lane segmentation map representative of one or more lanes detected in the image. For at least one of the wheels in the wheel segmentation map, the processor is also configured to determine a distance between the wheel and at least one nearby lane in the lane segmentation map. The processor is further configured to determine a distance between a vehicle in the image and the lane based on the distance between the wheel and the lane.

BACKGROUND Technological Field

The described technology generally relates to systems and methods fordetermining car to lane distance, and more particularly, to imageprocessing techniques for determining the distance between a car and alane.

Description of the Related Technology

In autonomous driving systems, the successful perception and predictionof the surrounding driving environment and traffic participants arecrucial for making correct and safe decisions for control of theautonomous or host vehicle. Visual perception techniques may includeobject recognition, two-dimensional (2D) or three-dimensional (3D)object detection and scene understanding. With the assistance offast-developing deep learning techniques and computational power (suchas graphics processing units (GPUs)), these visual perception techniquescan be successfully applied for use with autonomous or host vehicles.One aspect of scene understanding may include the determination of thelocation of other vehicles within the environment (e.g., the location ofa vehicle with respect to lane markers).

SUMMARY OF CERTAIN INVENTIVE ASPECTS

One inventive aspects is an in-vehicle control system, comprising: acamera configured to generate an image; a processor; and acomputer-readable memory in communication with the processor and havingstored thereon computer-executable instructions to cause the processorto: receive the image from the camera, generate a wheel segmentation maprepresentative of one or more wheels detected in the image, generate alane segmentation map representative of one or more lanes detected inthe image, for at least one of the wheels in the wheel segmentation map,determine a distance between the wheel and at least one nearby lane inthe lane segmentation map, and determine a distance between a vehicle inthe image and the lane based on the distance between the wheel and thelane.

Another aspect is a non-transitory computer readable storage mediumhaving stored thereon instructions that, when executed, cause at leastone computing device to: receive an image from a camera installed on anego vehicle; generate a wheel segmentation map representative of one ormore wheels detected in the image; generate a lane segmentation maprepresentative of one or more lanes detected in the image; for at leastone of the wheels in the wheel segmentation map, determine a distancebetween the wheel and at least one nearby lane in the lane segmentationmap; and determine a distance between a vehicle in the image and thelane based on the distance between the wheel and the lane.

Yet another aspect is a method for determining the distance between avehicle and a lane, comprising: receiving an image from a camerainstalled on an ego vehicle; generating a wheel segmentation maprepresentative of one or more wheels detected in the image; generating alane segmentation map representative of one or more lanes detected inthe image; for at least one of the wheels in the wheel segmentation map,determining a distance between the wheel and at least one nearby lane inthe lane segmentation map; and determining a distance between a vehiclein the image and the lane based on the distance between the wheel andthe lane.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example ecosystem including anin-vehicle control system and an image processing module in accordancewith aspects of this disclosure.

FIG. 2A is an example image obtained from a camera located on an egovehicle in accordance with aspects of this disclosure.

FIG. 2B includes another example image obtained from a camera and awheel segmentation map generated based on the example image inaccordance with aspects of this disclosure.

FIG. 3 is an example flow-chart illustrating an example method fordetermining the distance between a vehicle and lane(s) in accordancewith aspects of this disclosure.

FIGS. 4A-4C illustrate various images and segmentation maps which may begenerated during the method of FIG. 3.

FIGS. 5A-5C illustrate another embodiment of images and segmentationmaps which may be generated during the method of FIG. 3.

FIGS. 6A-6C illustrate yet another embodiment of images and segmentationmaps which may be generated during the method of FIG. 3.

FIG. 7 is an example flow-chart illustrating another example method fordetermining the distance between a vehicle and lane(s) in accordancewith aspects of this disclosure.

DETAILED DESCRIPTION OF THE CERTAIN INVENTIVE EMBODIMENTS

Introduction to in-Vehicle Control Systems

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method fordetermining car to lane distance are described herein. An exampleembodiment disclosed herein can be used in the context of an in-vehiclecontrol system 150 in a vehicle ecosystem 101. In one exampleembodiment, an in-vehicle control system 150 with an image processingmodule 200 resident in a vehicle 105 can be configured like thearchitecture and ecosystem 101 illustrated in FIG. 1. However, it willbe apparent to those of ordinary skill in the art that the imageprocessing module 200 described herein can be implemented, configured,and used in a variety of other applications and systems as well.

With continuing reference to FIG. 1, a block diagram illustrates anexample ecosystem 101 in which an in-vehicle control system 150 and animage processing module 200 of an example embodiment can be implemented.These components are described in more detail below. Ecosystem 101includes a variety of systems and components that can generate and/ordeliver one or more sources of information/data and related services tothe in-vehicle control system 150 and the image processing module 200,which can be installed in the vehicle 105. For example, a camerainstalled in the vehicle 105, as one of the devices of vehiclesubsystems 140, can generate image and timing data that can be receivedby the in-vehicle control system 150. The in-vehicle control system 150and the image processing module 200 executing therein can receive thisimage and timing data input. As described in more detail below, theimage processing module 200 can process the image input and extractobject features, which can be used by an autonomous vehicle controlsubsystem, as another one of the subsystems of vehicle subsystems 140.The autonomous vehicle control subsystem, for example, can use thereal-time extracted object features to safely and efficiently navigateand control the vehicle 105 through a real world driving environmentwhile avoiding obstacles and safely controlling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can reside in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can include a data processor 171 configured to execute the imageprocessing module 200 for processing image data received from one ormore of the vehicle subsystems 140. The data processor 171 can becombined with a data storage device 172 as part of a computing system170 in the in-vehicle control system 150. The data storage device 172can be used to store data, processing parameters, and data processinginstructions. A processing module interface 165 can be provided tofacilitate data communications between the data processor 171 and theimage processing module 200. In various example embodiments, a pluralityof processing modules, configured similarly to image processing module200, can be provided for execution by data processor 171. As shown bythe dashed lines in FIG. 1, the image processing module 200 can beintegrated into the in-vehicle control system 150, optionally downloadedto the in-vehicle control system 150, or deployed separately from thein-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data to/from a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing image input or image inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the image processing module 200 with the data network 120 viacellular, satellite, radio, or other conventional signal receptionmechanisms. Such cellular data networks are currently available (e.g.,Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-based data or contentnetworks are also currently available (e.g., SiriusXM™, HughesNet™,etc.). The broadcast networks, such as AM/FM radio networks, pagernetworks, UHF networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice over IP (VoIP) networks, and the like are alsoavailable. Thus, the in-vehicle control system 150 and the imageprocessing module 200 can receive web-based data or content via anin-vehicle web-enabled device interface 131, which can be used toconnect with the in-vehicle web-enabled device receiver 130 and network120. In this manner, the in-vehicle control system 150 and the imageprocessing module 200 can support a variety of network-connectablein-vehicle devices and systems from within a vehicle 105.

As shown in FIG. 1, the in-vehicle control system 150 and the imageprocessing module 200 can also receive data, image processing controlparameters, and training content from user mobile devices 132, which canbe located inside or proximately to the vehicle 105. The user mobiledevices 132 can represent standard mobile devices, such as cellularphones, smartphones, personal digital assistants (PDA's), MP3 players,tablet computing devices (e.g., iPad™), laptop computers, CD players,and other mobile devices, which can produce, receive, and/or deliverdata, image processing control parameters, and content for thein-vehicle control system 150 and the image processing module 200. Asshown in FIG. 1, the mobile devices 132 can also be in datacommunication with the network cloud 120. The mobile devices 132 cansource data and content from internal memory components of the mobiledevices 132 themselves or from network resources 122 via network 120.Additionally, mobile devices 132 can themselves include a GPS datareceiver, accelerometers, WiFi triangulation, or other geo-locationsensors or components in the mobile device, which can be used todetermine the real-time geo-location of the user (via the mobile device)at any moment in time. In any case, the in-vehicle control system 150and the image processing module 200 can receive data from the mobiledevices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the image processing module 200. The vehicle105 may include more or fewer subsystems and each subsystem couldinclude multiple elements. Further, each of the subsystems and elementsof vehicle 105 could be interconnected. Thus, one or more of thedescribed functions of the vehicle 105 may be divided up into additionalfunctional or physical components or combined into fewer functional orphysical components. In some further examples, additional functional andphysical components may be added to the examples illustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The term wheel may generally refer to a structure comprising arim configured to be fixedly attached to a tire, which is typicallyformed of rubber. Optionally, a wheel may include a hubcap attached toan outer surface of the rim or the tire may be exposed to theenvironment without the inclusion of a hubcap. As used herein, thedetection and/or segmentation of a wheel within an image may include thedetection of the entire wheel/tire combination, including the rubbertire and the central wheel, when visible.

The wheels of a given vehicle may represent at least one wheel that isfixedly coupled to the transmission and at least one tire coupled to arim of the wheel that could make contact with the driving surface. Thewheels may include a combination of metal and rubber, or anothercombination of materials. The transmission may include elements that areoperable to transmit mechanical power from the engine to the wheels. Forthis purpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an 02 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit can be configured to operate in a coherent(e.g., using heterodyne detection) or an incoherent detection mode. Thecameras may include one or more devices configured to capture aplurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the image processing module 200, the GPS transceiver, and one ormore predetermined maps so as to determine the driving path for thevehicle 105. The autonomous control unit may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 105. In general,the autonomous control unit may be configured to control the vehicle 105for operation without a driver or to provide driver assistance incontrolling the vehicle 105. In some embodiments, the autonomous controlunit may be configured to incorporate data from the image processingmodule 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, andother vehicle subsystems to determine the driving path or trajectory forthe vehicle 105. The vehicle control system 146 may additionally oralternatively include components other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, capabilities for a user/occupant of the vehicle105 to interact with the other vehicle subsystems. The visual displaydevices may provide information to a user of the vehicle 105. The userinterface devices can also be operable to accept input from the user viaa touchscreen. The touchscreen may be configured to sense at least oneof a position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providecapabilities for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 142, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 142, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 and theimage processing module 200, move in a controlled manner, or follow apath or trajectory based on output generated by the image processingmodule 200. In an example embodiment, the computing system 170 can beoperable to provide control over many aspects of the vehicle 105 and itssubsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, and imageprocessing module 200, as being integrated into the vehicle 105, one ormore of these components could be mounted or associated separately fromthe vehicle 105. For example, data storage device 172 could, in part orin full, exist separate from the vehicle 105. Thus, the vehicle 105could be provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 105could be communicatively coupled together in a wired or wirelessfashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the image processing module200 based on a variety of factors including, the context in which theuser is operating the vehicle (e.g., the location of the vehicle, thespecified destination, direction of travel, speed, the time of day, thestatus of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein.

In a particular embodiment, the in-vehicle control system 150 and theimage processing module 200 can be implemented as in-vehicle componentsof vehicle 105. In various example embodiments, the in-vehicle controlsystem 150 and the image processing module 200 in data communicationtherewith can be implemented as integrated components or as separatecomponents. For example, the image processing module 200 can be includedas a set of instructions stored in a non-transitory computer readablemedium, such as the data storage device 172, for causing the dataprocessor 171 to perform various image processing functionality. In anexample embodiment, the software components of the in-vehicle controlsystem 150 and/or the image processing module 200 can be dynamicallyupgraded, modified, and/or augmented by use of the data connection withthe mobile devices 132 and/or the network resources 122 via network 120.The in-vehicle control system 150 can periodically query a mobile device132 or a network resource 122 for updates or updates can be pushed tothe in-vehicle control system 150.

Car to Lane Distance Determination

In the various example embodiments disclosed herein, a system and methodfor determining the distance between a vehicle and a nearby lane markerare provided. The distance between a given vehicle within the field ofview of a camera of the ego-vehicle and the nearest lane(s) to the givenvehicle can be used as input information to a number of differentestimation, prediction, and/or detection technique. As used herein, theterm “ego-vehicle” is generally used to refer to the vehicle on whichthe described car to lane distance techniques are run (e.g., the vehicle105 illustrated in FIG. 1).

The distance between a vehicle and its nearby lane(s) can providecrucial information for accurate vehicle pose estimation, vehicleintention prediction, or lane-changing event detection. Accuratecar-lane distance can be an important determination for the autonomousdriving system, as this distance can provide direct information whichcan be used to determine one or more of: a) the pose/direction of thevehicle (e.g., by calculating the angle between the vehicle and itsnearby lane(s)), b) the driving pattern of the vehicle (e.g., does thevehicle prefer to drive along specific side of its lane—is the vehiclezigzagging within its lane, etc.), c) a prediction of the vehicle'sintention (e.g., is the vehicle going to change lanes—is the vehiclegoing to exit the highway, etc.). Accordingly, aspects of thisdisclosure relate to the determination of the distance between visiblevehicles and the nearest lane(s). In certain aspects, as described indetail below, the location of a vehicle's wheels with respect to thelane markers can be used to determine the distance between the vehicleand the lanes.

In order to determine the distance between a vehicle and its nearbylane(s) based on an image captured by the a camera an ego-vehicle (e.g.,using a camera from the vehicle sensor subsystem 144), the imageprocessing module 200 determines the locations of each of the vehicleand the lanes within the image. One technique which may be used todetermine the location of a vehicle within a captured image includes theuse of deep learning-based object detection module, in which atwo-dimensional (2D) bounding box is generated for each of the detectedvehicles within the image. However, due to the perspective change in the2D image obtained from a camera, the 2D bounding boxes alone may notprovide an accurate representation of the distance between the vehiclesand the lane(s). For example, FIG. 2A is an example image obtained froma camera located on an ego vehicle in accordance with aspects of thisdisclosure.

Based on the image 201 of FIG. 2A, a processor, executing the imageprocessing module 200, may generate bounding boxes 205, 210, 215 foreach of the vehicles detected within the image 201. In certainembodiments, each of the bounding boxes 205, 210, 215 may represent a 2Drectangular area of the image in which the pixels of the correspondingdetected vehicle are located. There may be certain limitations todetermining the distance between a given one of the detected vehiclesand the nearby lane(s) based on the bounding boxes 205, 210, 215. Forexample, as shown in FIG. 2A, bounding box 205 may be wider than thewidth of the corresponding vehicle. Thus, the distance between the edgesof the bounding box 205 and the nearby lanes may be lower than theground truth distance between the vehicle and the lanes. Further, aswill be understood, due to the perspective of the camera, certainbounding boxes 210 and 215 may be closer to the nearby lane(s) than theground truth distance. For example, although the vehicle correspondingto the bounding box 215 is located within its lane, the right side ofbounding box 215 overlaps the nearby right-side lane due to theperspective of the vehicle.

Accordingly, aspects of this disclosure relate to techniques fordetermining the distance between a detected vehicle and its nearbylane(s) which address at least some of the above described limitations.One technique may include determining a three-dimensional (3D) boundingbox for at least some of the detected vehicles in the image 201 capturedvia a camera on the ego vehicle. A 3D bounding box may providerectangular cuboid bounding the vehicle and defining eight corners andthe direction of travel of the vehicle. Thus, the generation of a 3Dbounding box may overcome reduce the perspective distortions incalculating the distance between a vehicle and its nearby lane(s).However, the calculation of a 3D bounding box (e.g., using only a 2Dsource image such as image 201) may require significant resources tocalculate since the 3D bounding box calculation can include correctannotation of the 3D bounding box, which may require accuratemeasurement of extrinsic and intrinsic camera parameter as well as themotion of ego-vehicle. In certain implementations, sufficiently accuratemeasurement of extrinsic and intrinsic camera parameter and the motionof ego-vehicle may not be possible due to hardware and/or softwarelimitations of the vehicle ecosystem 101. Additionally, it may bedifficult to obtain the ground truth (e.g., image recognition trainingdata and measurements of vehicle position) of the vehicles in an image201 in order to properly train image processing module 200 to generatesufficiently accurate 3D bounding boxes.

Another technique for determining the distance between a detectedvehicle and its nearby lane(s) includes detecting and identifying thewheels of vehicle in a received image 201 and using the detected wheelsto measure the distance between the vehicle and the nearby lane(s). Theuse of a vehicles wheel location to determine car to lane distance mayimprove the distance measurement over other techniques (e.g., 2Dbounding box based techniques) since the wheels are in contact with theroad surface. Due to the contact between the wheels and the roadsurface, any 2D perspective distortions are substantially reduced oreven eliminated from the distance calculation. The location of thewheels on a vehicle can also provide robust estimate of the vehicle posesince vehicles typically have 4 or more wheels to serve as referencepoints. Additionally, the image processing module 200 may be able tomore easily detect the wheels of a vehicle than produce a 3D boundingbox using a deep-learning based wheel segmentation module.

In certain embodiments, the image processing module 200 may beconfigured to detect wheels from within a received image 201 and producea segmentation map indicative of the location(s) of the wheel(s)detected in the image 201. FIG. 2B includes another example imageobtained from a camera and a wheel segmentation map generated based onthe example image in accordance with aspects of this disclosure. Asshown in FIG. 2B, the image 231 received from a camera may include anumber of vehicles including wheels which may be segmented by the imageprocessing module 200. The wheel segmentation process may produce awheel segmentation map 251 including an indication of the pixelsrepresentative of wheel(s) in the received image 231. As shown in FIG.2B, the identified wheels may correspond to all visible portions of thewheels in the image 231, including the tires and central wheel orhubcap. As will be described in detail below, the pixels of a givenwheel which are closest to the bottom of the image may be determined tobe in contact with the road surface.

The image processing module 200 may also be configured to detect thelane(s) within a received image 201 using, for example, a deeplearning-based lane detection technique. The lane detection techniquemay produce a segmentation map indicative of the location(s) of thelane(s) detected in the image 201. Each of the segmentation maps maycomprise pixel level representations of the detected wheel(s) andlane(s) respectively located within the wheel segmentation and lanesegmentation maps.

FIG. 3 is an example flow-chart illustrating an example method fordetermining the distance between a vehicle and lane(s) in accordancewith aspects of this disclosure. The method 300 illustrated in FIG. 3may be performed by an in-vehicle control system (such as the in-vehiclecontrol system 150 of FIG. 1) or component(s) thereof. For example, thesteps of method 300 illustrated in FIG. 3 may be performed byprocessor(s) and/or other component(s) of an in-vehicle control systemor associated system(s). For convenience, the method 300 is described asperformed by the processor of the in-vehicle control system.

The method 300 begins at block 301. At block 305, the processor receivesan image from a camera, which may be included as a component of thevehicle sensor subsystem 144. The image may include one or more vehiclesand one or more lane markings dividing the road into a plurality oflanes. At optional block 310, the processor generates a bounding box foreach vehicle detected in the received image. This may include, forexample, detecting pixels included in at least one of vehicle in theimage and determining 2D boundaries which encompass a majority of thepixels included in the vehicle. While in certain embodiments, theprocessor may use the bounding box to associate one or more detectedwheels with a given vehicle, in other embodiments, the processor may beconfigured to determine the distance between a detected wheel and a lanewithout the use of a bounding box.

At block 315, the processor generates a wheel segmentation map for thereceived image. The wheel segmentation map may include an indication ofeach pixel within the image that is detected to be a portion of a wheel.At block 320, the processor generates a lane segmentation map for thereceived image. The lane segmentation map may include an indication ofeach pixel within the image that is detected to be a portion of a lanemarking.

At block 325, the processor determines the distance between the wheel(s)in the wheel segmentation map and the wheel(s) nearby lane(s) from thelane segmentation map. In certain implementations, the distance betweena wheel and a nearby lane may be determined based on the number ofpixels located between the wheel in the wheel segmentation map and thelane in the lane segmentation map. As will be described below, theprocessor may use the determined distance between a wheel and a lane asan indication of the distance between a vehicle and the lane (e.g., avehicle bounded by a bounding box overlapping with the wheel in thewheel segmentation map). The method ends at block 330.

FIGS. 4A-4C illustrate various images and segmentation maps which may begenerated during the method 300 of FIG. 3. In particular, FIG. 4A is anexample 2D bounding box map corresponding to a received image inaccordance with aspects of this disclosure. FIG. 4B is an example wheelsegmentation map corresponding to a received image in accordance withaspects of this disclosure. FIG. 4B also illustrates the modifiedbounding box of the detected vehicles. FIG. 4C is an example lanesegmentation map corresponding to a received image in accordance withaspects of this disclosure.

Referring first to FIG. 4A, the 2D bounding box map 401 may include aplurality of bounding boxes 405 which bound and/or surround the pixelsof each of the detected vehicles within the image. The bounding box map401 may be a map generated by the processor during step 310 of method300. In FIG. 4B, the wheel segmentation map 431 includes a plurality ofgroups of pixels representing the wheels 435 as generated, for example,by step 315 of method 300. Also shown in the wheel segmentation map 431is a set of trapezoids 440 which may be generated based on the detectedwheels 435. Technique(s) related to generating the trapezoids 440 willbe described in detail below.

FIG. 4C illustrates a lane segmentation map 461 including groups ofpixels representing the lanes 465 as generated, for example, by step 320of method 300. Also shown in FIG. 4C is one embodiment of the distancevalues 470 which may be calculated at step 325 of method 300. While thedistance values 470 are illustrated as being overlaid on the lanesegmentation map 461 in the embodiment of FIG. 4C, depending on theembodiment, these values may not be positioned on the map, but rather,provided as an output of the method 300. Thus, the distance values canbe used by the in-vehicle control system to determine, for example, aprediction of the behavior of the corresponding vehicle (e.g., byanalyzing changes in the distance between the vehicle and its nearbylane(s) over time).

FIGS. 5A-5C and 6A-6C illustrate additional embodiments of images andsegmentation maps which may be generated during the method 300 of FIG.3. In particular, FIGS. 5A and 6A provide example 2D bounding box maps501, 601 corresponding to received images, FIGS. 5B and 6B provideexample wheel segmentation maps 531, 631 corresponding to the receivedimages, and FIGS. 5C and 6C provide example lane segmentation maps 561,661 corresponding to the received images. FIGS. 5A-5C illustrate anembodiment where the lanes curve while FIGS. 6A-6C illustrate anembodiment where one of a vehicle's rear wheels is occluded. Since theremaining features of FIGS. 5A-5C and 6A-6C are substantially similar tothe features of FIGS. 4A-4C, additional reference numerals and adetailed description thereof is omitted for the sake of clarity.

FIG. 7 is an example flow-chart illustrating another example method fordetermining the distance between a vehicle and lane(s) in accordancewith aspects of this disclosure. The method 700 illustrated in FIG. 7may be performed by an in-vehicle control system (such as the in-vehiclecontrol system 150 of FIG. 1) or component(s) thereof. For example, thesteps of method 700 illustrated in FIG. 7 may be performed byprocessor(s) and/or other component(s) of an in-vehicle control systemor associated system(s). For convenience, the method 700 is described asperformed by the processor of the in-vehicle control system.

In certain embodiments, the method 700 may include a number of stepsperformed by the processor in performing step 325 of method 300. Thus,the method 700 may comprise a technique for determining the distancebetween a vehicle detected within an image received from a camera andthe vehicle's nearest lane(s) based on a wheel segmentation map and alane segmentation map.

The method 700 begins at block 701. At block 705, the processorassociates the wheels identified in a wheel segmentation map withcorresponding bounding boxes of a bounding box map. For example, theprocessor may associate wheels detected in the wheel segmentation withthe bounding box in response to the wheels at least partiallyoverlapping the bounding box. In certain implementations, the wheelsegmentation map is generated on a frame-by-frame basis based on imagesreceived from the camera. By associating the detected wheels with agiven bounding box, the processor may be able to make certaindeterminations regarding the location of the detected wheels on thevehicle. For example, the processor may be able to determine whether agiven wheel is a rear wheel or a front wheel of the vehicle and/orwhether the wheel is a left-most wheel or a right-most wheel of thevehicle. In one implementation, the association of the wheels with thebounding boxes may include cropping an area in the wheel segmentationmap corresponding to a selected bounding box from the bounding box map.

At block 710, the processor counts the number of wheels within thecropped area by calculating the number of groups of connected pixelswithin the cropped image. The processor may be further configured todetermine the left and right sides of the vehicle based on the number ofwheels detected within the cropped wheel segmentation map. Inparticular, the technique used to determine the distance between thevehicle and the nearby lane(s) may depend on the number of wheels, andtheir locations, within the bounding box. A number of example techniquesdepending on the number of wheels detected at block 710 are outlinedbelow.

1. No Wheels are Detected within the Cropped Area

When no wheels are detected within the cropped area, the processor mayinfer that i) the vehicle is at least partially occluded by anotherobject within the image, ii) the vehicle is beyond a threshold distanceahead of the ego vehicle, such that the wheels of the detected vehicleare not detectable based on the received image, or iii) the detectedvehicle and bounding box are reflective of a false positive detection.Since no wheels are detected within the bounding box, the processor mayset the left and right bottom corners of the bounding box to beindicative of the left and right sides of the vehicle.

2. Only One Wheel is Detected within the Cropped Area

When only one wheel is detected within the cropped area, the processormay infer that the vehicle is only partially in view. The processor maydetermine whether the vehicle is occluded when only one wheel isdetected. Occlusion detection is performed based on comparing theposition of the bottom edges of the two bounding boxes. If one edge ishigher than the other, then the processor may determine that thecorresponding vehicle is occluded. In response to determining that thevehicle is occluded, the processor may set the bottom left corner andbottom right corner of the bounding box to represent the left and rightsides of the vehicle. In response to determining that the vehicle is notoccluded, the processor may set the leftmost pixel and the rightmostpixel of the detected wheel to represent the left and right side of thevehicle. While the use of a single wheel may not be representative ofthe actual left and right sides of the entire vehicle, the distancebetween the detected wheel and the nearby lane(s) may still be useful.In certain embodiments, the processor may also provide an indicationthat only a single wheel has been used to determine the distance betweenthe car and the nearby lanes when outputting the distance measurements.

3. Two Wheels are Detected within the Cropped Area

When two wheels are detected within the cropped area, the processor maytreat each of the detected wheels separately as representing the leftand right sides of the vehicle. For each wheel, the processor maydetermine the distance (e.g., based on the number of pixels) between thebottom pixel of each column of pixels in the detected wheel and thebottom edge of the 2D bounding box. Thus, the pixels which are closestto the bottom of the bounding box may indicative of the pixels being incontact with the road surface. In certain embodiments, the processor maytake the average value (e.g., pixel location) of all columns of pixelsthat have the minimum distance to the bottom of the bounding box asrepresenting either the left or right side of the vehicle.

4. More than Two Wheels are Detected within the Cropped Area

When more than two wheels are detected within the cropped area, theprocessor may select two of the detected wheels to be representative ofthe left and right sides of the vehicle. In one embodiment, theprocessor selects the wheels that have pixels located in the leftmostand rightmost columns. That is, the processor may select the wheelswhich are located at the leftmost and rightmost sides of the boundingbox as the two wheels. However, when two or more wheels share pixels inthe same column of pixels of the image, the processor may select thebottom-most wheel to represent the corresponding side (e.g., left orright side) of the vehicle. Once the processor has select two of thedetected wheels, the processor may define the left and right sides ofthe vehicle in a manner similar to the above-described technique forwhen two wheels are detected within the cropped area.

Once the left and right sides of the vehicle have been determined basedon the number of detected wheels as described above, the processor may,at block 715, determine a trapezoid representative of the left and/orright side of the vehicle. This may include, for example, the processorreducing the size of the bounding box to the shape of a trapezoid havingtwo bottom vertices defined by the left and right sides of the vehicleas determined by block 710. The processor may determine the location ofa given wheel based on the pixels of the wheel which are closest to thebottom of the image (or closest to the bottom of the correspondingbounding box). That is, the lowest portion of a detected wheel may beinferred to be in contact with the road surface (or at least closeenough to the portion of the wheel in contact with the road surface forthe purpose of estimating the wheel's position within its lane). Whenmore than one pixels is determined to be the closest to the bottom ofthe bounding box (e.g., the pixels are in the same row), the processormay take the average value of the pixel locations for the pixels thatare the closest to the bottom of the bounding box as the location of thewheel.

Embodiments of the trapezoids are illustrated in FIGS. 4B, 5B, and 6B.For example, as shown in FIGS. 4B and 5B, the trapezoids may be definedwith respect to the bottom wheels detected for a given vehicle. In FIG.7B, the bottom right wheel of the right side vehicle is not visible, andthus, the trapezoid may be defined based on the two left wheelsdetected.

At block 720, the processor determines the distance between the vehicleand its nearby lane(s) based on the trapezoid determined in block 715and the lane segmentation map. In certain implementations, the processormay determine the coordinates of the pixels (e.g., the row and columnfor each of the pixels) representing the bottom two vertices of thetrapezoid. For example, the bottom-left vertex of the trapezoid may havea coordinate (row r, column c) in the image. Continuing the example, forrow r, the processor may obtain the corresponding values of all columnsin the same row (e.g., row r) for the lanes identified in the lanesegmentation map. The processor may then locate the individual lanes inthe same row (e.g., row r) by selecting the center and edge pixels foreach lane in the same row and sorting all of the selected centers andedges based on their column values. In certain implementations, theprocessor may determine the lane widths based on the center locations.The processor may compare the column values of the vertices of thetrapezoid (e.g., the column value for the bottom-left vertex in theexample) to the sorted center values for the lanes in the same row.

The processor may then determine the distance, in pixels, between thevertex and the closest lane centers to the left and right of the vertex.For each of the closest left and right lanes, the processor may alsodetermine the ratio between the distance in pixels to the lane and thetotal lane width measured in pixels.

Depending on the location of a given vertex of the trapezoid, thelocation of the vertex can be classified into three main categories. Thevertex may be: i) located on the closest lane (e.g., overlapping withthe closest lane), ii) located between two lanes, or iii) one of the twoclosest lanes may be missing (e.g., at least partially occluded fromview in the same row as the vertex or missing entirely from the image).

When a given vertex of the trapezoid is located on the closest lane, theprocessor may calculate a distance of 0 for the closest lane and a pixeldistance equal to the lane width for the second closest lane. Theprocessor may determine the ratio between the distance in pixels to thelane and the total lane width as 0 for the closest lane and 1 for thesecond closest lane.

When a given vertex of the trapezoid is located between lanes, theprocessor may calculate the distance between the pixels based on theabsolute difference between the column coordinate of the vertex and thecolumn coordinates of the centers of the nearest two lanes. Theprocessor may determine the ratios based on the distances in pixelsbetween the vertex and the lane centers and the lane width, measured inpixels, for the same row as the row of the vertex.

When one of the nearest lanes is missing for a given vertex of thetrapezoid, the processor may output an indication that the distance tothe missing side cannot be calculated. For example, the processor mayoutput a value of −1 to be indicative of a missing lane. The processormay also provide an indication that at least one of the lanes is missing(e.g., by outputting a value of −1) for the ratio value since the widthof the lane cannot be calculated. The method ends at block 725.

While there have been shown and described and pointed out thefundamental novel features of the invention as applied to certaininventive embodiments, it will be understood that the foregoing isconsidered as illustrative only of the principles of the invention andnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Modifications or variations are possible in light ofthe above teachings. The embodiments discussed were chosen and describedto provide the best illustration of the principles of the invention andits practical application to enable one of ordinary skill in the art toutilize the invention in various embodiments and with variousmodifications as are suited to the particular use contemplate. All suchmodifications and variations are within the scope of the invention asdetermined by the appended claims when interpreted in accordance withthe breadth to which they are entitled.

What is claimed is:
 1. An in-vehicle control system, comprising: acamera configured to generate an image; a processor; and acomputer-readable memory in communication with the processor and havingstored thereon computer-executable instructions to cause the processorto: receive the image from the camera, generate a wheel segmentation maprepresentative of one or more wheels detected in the image, generate alane segmentation map representative of one or more lanes detected inthe image, for at least one of the wheels in the wheel segmentation map,determine a distance between the wheel and at least one nearby lane inthe lane segmentation map, and determine a distance between a vehicle inthe image and the lane based on the distance between the wheel and thelane.
 2. The system of claim 1, wherein the memory further has storedthereon computer-executable instructions to cause the processor to:detect a group of pixels representing at least one vehicle in the image,and for each of the detected vehicles: generate a two-dimensional (2D)bounding box that bounds the group of pixels representing the vehicle,determine that at least one of the wheels in the wheel segmentation mapoverlap the 2D bounding box, and associate the overlapping at least onewheel with the 2D bounding box.
 3. The system of claim 2, wherein thememory further has stored thereon computer-executable instructions tocause the processor to, for each of the detected vehicles: determine thenumber of the wheels that overlap the 2D bounding box, wherein thedetermination of distance the between the vehicle and the lane isfurther based on the number of the wheels that overlap the 2D boundingbox.
 4. The system of claim 3, wherein the memory further has storedthereon computer-executable instructions to cause the processor to, foreach of the detected vehicles: determine a trapezoid representative ofthe left and right sides of the vehicle based on a location of thewheels that overlap the 2D bounding box, select a bottom vertex of thetrapezoid, determine a number of pixels separating the bottom vertex andthe lane, and determine the distance between the vehicle and the lanebased on the number of pixels.
 5. The system of claim 4, whereindetermining the number of pixels separating the bottom vertex and thelane comprises determining the number of pixels separating the bottomvertex and a center of the lane in the same row of pixels and the bottomvertex.
 6. The system of claim 4, wherein determining the trapezoidcomprises: determine that at least two of the wheels in the wheelsegmentation map overlap the 2D bounding box, select a first one of theat least two wheels as a bottom-left wheel of the vehicle, select asecond one of the at least two wheels as a bottom-right wheel of thevehicle, and generate the trapezoid having a bottom-left vertex based ona location of the bottom-left wheel and a bottom-right vertex based on alocation of the bottom-right wheel.
 7. The system of claim 1, whereindistance between a vehicle in the image and the lane comprises i) anabsolute distance between the wheel and the lane and ii) a ratio betweenthe absolute distance and the width of the lane.
 8. The system of claim1, wherein the memory further has stored thereon computer-executableinstructions to cause the processor to: determine a predicted behaviorof the vehicle based on the distance between the vehicle and the lane.9. A non-transitory computer readable storage medium having storedthereon instructions that, when executed, cause at least one computingdevice to: receive an image from a camera installed on an ego vehicle;generate a wheel segmentation map representative of one or more wheelsdetected in the image; generate a lane segmentation map representativeof one or more lanes detected in the image; for at least one of thewheels in the wheel segmentation map, determine a distance between thewheel and at least one nearby lane in the lane segmentation map; anddetermine a distance between a vehicle in the image and the lane basedon the distance between the wheel and the lane.
 10. The non-transitorycomputer readable storage medium of claim 9, further having storedthereon instructions that, when executed, cause at least one computingdevice to: detect a group of pixels representing at least one vehicle inthe image, and for each of the detected vehicles: generate atwo-dimensional (2D) bounding box that bounds the group of pixelsrepresenting the vehicle, determine that at least one of the wheels inthe wheel segmentation map overlap the 2D bounding box, and associatethe overlapping at least one wheel with the 2D bounding box.
 11. Thenon-transitory computer readable storage medium of claim 10, furtherhaving stored thereon instructions that, when executed, cause at leastone computing device to, for each of the detected vehicles: determinethe number of the wheels that overlap the 2D bounding box, wherein thedetermination of distance the between the vehicle and the lane isfurther based on the number of the wheels that overlap the 2D boundingbox.
 12. The non-transitory computer readable storage medium of claim11, further having stored thereon instructions that, when executed,cause at least one computing device to, for each of the detectedvehicles: determine a trapezoid representative of the left and rightsides of the vehicle based on a location of the wheels that overlap the2D bounding box, select a bottom vertex of the trapezoid, determine anumber of pixels separating the bottom vertex and the lane, anddetermine the distance between the vehicle and the lane based on thenumber of pixels.
 13. The non-transitory computer readable storagemedium of claim 12, wherein determining the number of pixels separatingthe bottom vertex and the lane comprises determining the number ofpixels separating the bottom vertex and a center of the lane in the samerow of pixels and the bottom vertex.
 14. The non-transitory computerreadable storage medium of claim 12, further having stored thereoninstructions that, when executed, cause at least one computing deviceto: determine that at least two of the wheels in the wheel segmentationmap overlap the 2D bounding box, select a first one of the at least twowheels as a bottom-left wheel of the vehicle, select a second one of theat least two wheels as a bottom-right wheel of the vehicle, and generatethe trapezoid having a bottom-left vertex based on a location of thebottom-left wheel and a bottom-right vertex based on a location of thebottom-right wheel.
 15. A method for determining the distance between avehicle and a lane, comprising: receiving an image from a camerainstalled on an ego vehicle; generating a wheel segmentation maprepresentative of one or more wheels detected in the image; generating alane segmentation map representative of one or more lanes detected inthe image; for at least one of the wheels in the wheel segmentation map,determining a distance between the wheel and at least one nearby lane inthe lane segmentation map; and determining a distance between a vehiclein the image and the lane based on the distance between the wheel andthe lane.
 16. The method of claim 15, further comprising: detecting agroup of pixels representing at least one vehicle in the image, and foreach of the detected vehicles: generating a two-dimensional (2D)bounding box that bounds the group of pixels representing the vehicle,determining that at least one of the wheels in the wheel segmentationmap overlap the 2D bounding box, and associating the overlapping atleast one wheel with the 2D bounding box.
 17. The method of claim 16,further comprising, for each of the detected vehicles: determining thenumber of the wheels that overlap the 2D bounding box, wherein thedetermination of distance the between the vehicle and the lane isfurther based on the number of the wheels that overlap the 2D boundingbox.
 18. The method of claim 17, further comprising, for each of thedetected vehicles: determining a trapezoid representative of the leftand right sides of the vehicle based on a location of the wheels thatoverlap the 2D bounding box, selecting a bottom vertex of the trapezoid,determining a number of pixels separating the bottom vertex and thelane, and determining the distance between the vehicle and the lanebased on the number of pixels.
 19. The method of claim 18, whereindetermining the number of pixels separating the bottom vertex and thelane comprises determining the number of pixels separating the bottomvertex and a center of the lane in the same row of pixels and the bottomvertex.
 20. The method of claim 18, further comprising: determining thatat least two of the wheels in the wheel segmentation map overlap the 2Dbounding box, selecting a first one of the at least two wheels as abottom-left wheel of the vehicle, selecting a second one of the at leasttwo wheels as a bottom-right wheel of the vehicle, and generating thetrapezoid having a bottom-left vertex based on a location of thebottom-left wheel and a bottom-right vertex based on a location of thebottom-right wheel.